Identifying causal genetic variants and molecular mechanisms impacting mental health

识别影响心理健康的因果遗传变异和分子机制

• 批准号: 10571911
• 负责人: Anshul Kundaje
• 金额: $ 61.6万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10571911
• 关键词: ATAC-seq; Acute; Affect; Alleles; Alzheimer' s Disease; Biological Assay; Biology; Brain; Brain region; Catalogs; Cells; Chromatin; Collaborations; Communities; Computer software; Computing Methodologies; DNA Sequence; Data; Databases; Detection; Development; Disease; Future; Gene Expression; Gene Expression Regulation; Gene Frequency; Genes; Genetic; Genetic Diseases; Genetic Load; Genetic Risk; Genetic Variation; Genotype-Tissue Expression Project; Human; Individual; Influentials; Learning; Link; Linkage Disequilibrium; Machine Learning; Maps; Masks; Mental Health; Mental Health Associations; Mental disorders; Methods; Modeling; Molecular; Molecular Disease; Mus; National Human Genome Research Institute; Neurodevelopmental Disorder; Organoids; Outcome; Parkinson Disease; Pathologic; Performance; Population; Quantitative Trait Loci; Reporter; Reproducibility; Research; Resolution; Resources; Risk; Tissue Sample; Tissues; Training; Uncertainty; Untranslated RNA; Variant; Weight; Work; base; biobank; brain cell; causal variant; cell type; computational pipelines; deep learning model; disorder risk; epigenome; epigenomics; experimental study; functional genomics; genetic variant; genome resource; genome wide association study; genomic locus; improved; insight; machine learning method; machine learning model; novel; novel strategies; open data; polygenic risk score; prevent; public health relevance; regression algorithm; risk prediction; success; therapy development; trait

Identifying how genetic variation leads to neurodevelopmental or psychiatric disorders provides new means to study, predict, prevent and treat disease. Identifying the immediate molecular consequences of disease- associated genetic variation has necessitated the development of large-scale, multi-tissue functional genomic resources. Projects such as GTEx, Roadmap Epigenomics Project and PsychENCODE have combined molecular QTL mapping and epigenomic maps in bulk tissues to interpret various disease-associated genetic variants. However, few colocalizations between molecular QTLs and traits have been robustly identified and few causal variants mapped. As tissues like the brain constitute 100s of cell-types, we hypothesize that existing maps may mask the contributions of disease-associated variation in less-abundant cell types. One extremely powerful approach to identify cell-type specific molecular effects and their relationship to genetic diseases is through application of chromatin accessibility data – these data both allow inference of causal cell types and provide base level resolution gene regulation. Our team has considerable expertise in connecting GWAS to molecular functions and predicting causal variants through use of chromatin accessibility data. We have additionally recently collaborated to generate a comprehensive, multi-individual map single cell ATAC- seq map (scATAC-seq) of six different brain regions to detect causal cell types and predict causal variants. This work has been recently demonstrated in our fine-mapping study of Alzheimer’s and Parkinson’s disease (Corces et al, bioRxiv, 2020) but has not been systematically applied to mental health disorders. We propose to develop statistical genetics and machine learning approaches that advance the use of scATAC-seq data to connecting mental health GWAS loci to specific cell types, mechanisms and causal variants. In Aim 1, we will assemble a pipeline that leverages region and cell type-specific scATAC-seq data to identify pathological cell types for 100s of mental health and brain-related traits. We will also enhance the detection of cell-type specific molecular mechanisms by extending and applying a novel GWAS/QTL colocalization approach. Throughout these activities, variants will be validated using massively-parallel reporter assays (MPRA). In Aim 2, we will develop sophisticated machine learning models that learn regulatory grammars and score variants across the allele frequency spectrum. Predicted causal variants in GWAS loci will be further assessed with MPRAs in Aim 1 and applied in Aim 3. In Aim 3, we will demonstrate how improved detection of causal variants using our single-cell informed models aids transferability of polygenic risk scores across populations. We will provide open resources and reproducible computational methods and pipelines that integrate single cell chromatin accessibility data from multiple brain regions. This will allow detection cell-type specific genetic effects and pathological cell types in mental health GWAS, establish robust causal links between variants, genes and disease, and improve prediction of disease risk.

识别基因变异如何导致神经发育或精神障碍提供了新的手段 研究、预测、预防和治疗疾病。确定疾病的直接分子后果- 相关的遗传变异使得大规模、多组织功能基因组的发展成为必要 资源。GTEx、路线图表观基因组学项目和心理编码等项目已经合并 大块组织分子QTL定位和表观基因组图谱解释多种疾病相关遗传 变种。然而，很少有分子QTL和性状之间的共定位被强有力地识别出来。 找到的因果变异很少。由于像大脑这样的组织由100种细胞类型组成，我们假设 现有的图谱可能掩盖了不太丰富的细胞类型中与疾病相关的变异的贡献。一 识别细胞类型特异性分子效应及其与遗传关系的极其有效的方法 疾病是通过应用染色质可及性数据--这些数据都允许推断因果细胞 类型，并提供基准级分辨率基因调控。我们的团队在连接方面拥有相当多的专业知识 通过使用染色质可及性数据对分子功能和预测因果变异进行研究。我们 最近还合作生成了一个全面的、多个个体的地图单元格ATAC- 六个不同大脑区域的序列图(scatac-seq)，以检测因果细胞类型并预测因果变异。 这项工作最近在我们对阿尔茨海默氏症和帕金森氏病的精细地图研究中得到了演示 (Corce等人，BioRxiv，2020)，但尚未系统地应用于精神健康障碍。我们建议 开发统计遗传学和机器学习方法，以促进SCATAC-SEQ数据的使用 将心理健康基因座与特定的细胞类型、机制和因果变异联系起来。在目标1中，我们将 组装利用区域和细胞类型特定的scatac-seq数据来识别病理细胞的管道 心理健康和与大脑相关的特征的类型。我们还将加强对细胞类型特异性的检测 通过扩展和应用一种新的GWAS/QTL共定位方法来研究分子机制。贯穿始终 这些活动、变异体将使用大规模平行报告分析(MPRA)进行验证。在目标2中，我们将 开发复杂的机器学习模型，以学习法规语法并为 等位基因频谱。将在AIM中使用MPRAS进一步评估GWAS基因座中预测的因果变异 1并应用于目标3。在目标3中，我们将演示如何使用我们的 单细胞信息模型有助于多基因风险评分在人群中的可转移性。 我们将提供开放的资源和可重复使用的计算方法和管道 来自多个脑区的单细胞染色质可及性数据。这将允许检测特定的单元格类型 遗传效应和心理健康GWA中的病理细胞类型之间建立了牢固的因果联系 变异、基因和疾病，并改进对疾病风险的预测。